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CIS Seminars & Events

Fall 2017 Colloquium Series

Unless otherwise noted, our lectures are held weekly on Tuesday or Thursday from 3:00 p.m. to 4:15 p.m. in
Wu and Chen Auditorium,
Levine Hall.

September 7th

Hovav Shacham
Department of Computer Science and Engineering
University of California, San Diego
"Trusted Browsers for Uncertain Times"

Read Abstract and Bio

Abstract
The Web is the most important platform for human communication, including communication by human-rights activists, reporters, dissidents, and others at risk of reprisal for their speech. Modern browsers are designed to keep their users’ computers from being compromised in “browse-by” attacks, they have no comparable architectural protections for users’ privacy. In this talk, I will show how the unintended interactions of browser features with underlying system components can be used — and are used — by malicious sites to track and identify Web users and to learn information shared by those users with other sites, in violation of the browser’s intended compartmentalization guarantees. Two decades of experience show that vendors’ ad-hoc efforts to close each hole as it is identified are unlikely to produce a browser that keeps its users’ secrets. Drawing on the trusted systems literature of the 1980s, I present a principled browser design that provably reduces the capacity of all timing channels that expose secret information.

Bio
Hovav Shacham is a Professor of Computer Science and Engineering at the University of California, San Diego. His research interests are in applied cryptography, systems security, privacy-enhancing technologies, and technology policy. Shacham was a student at Stanford and a postdoctoral fellow at the Weizmann Institute. He took part in California’s 2007 “Top-to-Bottom” voting systems review and served on the advisory committee for California’s 2011–13 post-election risk-limiting audit pilot program. His work has been cited by the Federal Trade Commission, the National Highway Traffic Safety Administration, and the RAND Corporation.

September 12th

Abstract
The Big Data revolution has been enabled by a wealth of innovation in software platforms for data storage, analytics, and machine learning. The design of Big Data platforms such as Hadoop and Spark focused on scalability, fault-tolerance and performance. As these and other systems increasingly become part of the mainstream, the next set of challenges are becoming clearer. Requirements for performance are changing as workloads and hardware evolve. But more fundamentally, other issues are moving to the forefront. These include ease of use for a wide range of users, security, concerns about privacy and potential bias, and the perennial problems of data quality and integration from heterogeneous sources. In this talk, I will give an overview of how we got here, with an emphasis on the development of the Apache Spark system. I will then focus on these emerging issues with an eye towards where the academic research community can most effectively engage.

Bio
Michael Franklin is the Liew Family Chair of Computer Science at the University of Chicago where he also serves as senior advisor to the provost on computation and data science. Previously he was at UC Berkeley where he was the Thomas M. Siebel Professor of Computer Science and Chair of the Computer Science Division. He co-founded Berkeley’s AMPLab, a leading academic big data analytics research center, and served as an executive committee member for the Berkeley Institute for Data Science, a campus-wide initiative to advance data science environments. Michael is a Fellow of ACM, a two-time recipient of the ACM SIGMOD “Test of Time” award, and the Outstanding Advisor award from the Berkeley CS Graduate
Student Association.

September 21st

Abstract
Gaurav will talk about some job trends in financial services to start and cover the transformational impact of A.I. and Deep Learning in making trading a scientific process. Deep learning has been very successful in social sciences and specially areas where there is a lot of data. Trading and investment management fits this paradigm perfectly. It is a social science and not a pure science, and we are generating petabytes of data everyday making it tough to learn from. With the advent of Deep Learning and Big Data technologies for efficient computation, we are finally able to use the same methods in investment management as we would in face recognition or making chatbots. This focus on learning a hierarchical set of concepts is truly making investing a scientific process, a utility.

Bio
Gaurav Chakravorty is a UPenn alumnus (MSE CIS '05) and co-founder and CIO at qplum. Qplum is an asset management firm that offers A.I. based trading strategies. Gaurav has been one of the early pioneers in machine learning based high-frequency trading. He built the most profitable algo trading group at Tower Research from 2005-2010 and was the youngest partner in the firm. Gaurav's strategies have made more than $1.4bln to-date. He believes in the potential of using Deep Learning to reduce fees and make investing a science that is universally accessible.

September 28th

Abstract
QuickCheck is a random testing tool that not only tests code against its specification, but also "shrinks" any failing tests to minimal counterexamples. Since 2006, Quviq has been helping customers apply this idea to industrial software. In this talk, I'll focus on the value of shrinking, showing how it can be used to simplify fault diagnosis, to avoid repeatedly testing for the same bugs, to refine the idea of "testíng a requirement" and to illustrate specifications with examples.

Bio
John Hughes has worked in the area of functional programming since around 1980, authoring one of the most widely read introductions to the area, "Why Functional Programming Matters", and helping to design Haskell. In 2000 he and Koen Claessen published "QuickCheck: A Lightweight Random Testing Tool for Haskell" at ICFP, which in 2010 received the "Most Influential Paper" award for that year. In 2006 he and Thomas Arts founded Quviq, a start-up based on the QuickCheck idea, and since then he has divided his time between Quviq and Chalmers University in Gothenburg, Sweden.

October 3rd

Abstract
Market watchers estimate the IoT Security marketplace is now valued at over $6 Billion and expected to reach $22 Billion by 2020. Just 5 years ago, embedded device security was barely on the map. Our early work in the IDS Lab at Columbia demonstrated the seriousness of the embedded device insecurity problem, and the relatively easy exploitation of devices such as printers, IP phones and routers. Recent advances in offensive technologies targeting a wide range of IoT devices have shown that the exploitation of these lucrative but poorly designed devices is not terribly difficult, including medical products, SCADA devices, automobiles and refrigerators. The goal of our early work was to defend embedded devices with an entirely new defensive capability we call the Software Symbiote, a host-based defensive technology that automatically injects intrusion detection functionality within the firmware of any device. In this talk we will provide a brief history of our work on the Symbiote technology, and the transition from academic research to practical and wide-spread use in common commodity products.

Bio
Salvatore Stolfo is a Professor of Computer Science at Columbia University. He is regarded as creating the area of machine learning applied to computer security in the mid-1990’s and has created several anomaly detection algorithms and systems addressing some of the hardest problems in securing computer systems. Of particular note is his recent interest in the practical application of deception security in scale. Stolfo is also co-inventor of the Symbiote technology that automatically injects intrusion detection functionality into arbitrary embedded devices. Stolfo has had numerous best papers and awards, most recently the RAID Most Influential Paper and Usenix Security Distinguished Paper awards. He has published well over 230 papers and has been granted over 60 patents and has been an advisor and consultant to government agencies, including DARPA, the National Academies and others, for well over 2 decades. Two security companies were recently spun out of his IDS lab, Allure Security Technology and Red Balloon Security.

October 12th

Abstract
New computing interfaces that use "natural" modes of interaction — such as multitouch and gestures — are rapidly becoming more popular than traditional keyboard-based interaction. These devices are being used to consume and directly interact with data in a wide range of contexts, from business intelligence to data-driven sciences. Applications for such devices are highly interactive, and pose a fundamentally different set of expectations on the underlying data infrastructure. In this talk, we rethink various aspects of the data infrastructure stack, from the query specification process to distributed query execution, to address interactive workloads. We explore the impact of including interactivity as first-class concept, and show that our methods result in experiences that are not only fluid, but also more intuitive for the end-user.

Bio
Arnab Nandi is an Associate Professor in the Computer Science and Engineering department at The Ohio State University. Arnab’s research is in the area of database systems, focusing challenges in large-scale data analytics and human-in-the-loop data exploration. Arnab is also a founder of The STEAM Factory, a collaborative interdisciplinary research and public outreach initiative, and faculty director of the OHI/O Informal Learning Program. Arnab is a recipient of the US National Science Foundation’s CAREER Award, a Google Faculty Research Award, and the Ohio State College of Engineering Lumley Research Award. He is also the 2016 recipient of the IEEE TCDE Early Career Award for his contributions towards user-focused data interaction.

October 17th

Juliana Freire
Department of Computer Science and Engineering
New York University

November 2nd

November 7th

Ehsan Hoque
Computer Science Department
University of Rochester
"When can a computer improve your social skills?"

Read Abstract and Bio

Abstract
Many people fear automation. They may see it as a potential job killer. They may also be concerned about what can be automated. Could we train a computer to teach us human skills? Should we? Artificial intelligence, when designed properly, can help people improve important social and cognitive skills. My research group has shown how automated systems can develop skills that improve performance in job interviews, public speaking, negotiations, working as part of a team, producing vowels during music
training, end-of-life communication between oncologists and cancer patients, and even routine social
interactions for people with Asperger’s syndrome. In this talk, I will offer insights gained from our exploration of several questions: How are humans able to improve important social and cognitive skills with a computer? What aspect of the feedback helps the most? How to design experiments to ensure that the skills generalize?

Bio
M. Ehsan Hoque is an assistant professor of computer science at the University of Rochester, where he leads the Rochester Human-Computer Interaction, or ROC HCI, Group. His group’s research focuses on understanding and modeling unwritten rules of human communication with applications in business communication, health, and assessment technologies. Ehsan received his Ph.D. from the MIT Media Lab in 2013 where his dissertation work was highlighted by the MIT Museum as one of the most unconventional inventions at MIT. Ehsan and his group’s work has received a Best Paper Award at Ubiquitous Computing (UbiComp 2013), Best Paper Honorable Mentions in Automated Face and Gesture Recognition (FG 2011) and Intelligent Virtual Agents (IVA 2006). Ehsan has received MIT TR35 Award(2016), World Technology Award (2016), and Google Faculty Award (2014, 2016). In 2017, Science News recognized him as one of 10 early to mid-career scientists to watch (the SN 10). Follow the group’s work on Twitter at @rochci.

November 9th

Konrad Kording
Department of Neuroscience, Department of Bioengineering
UPenn PIK Professor"Rethinking the role of machine learning in (neuro)science"

Read Abstract and Bio

Abstract
The goal of much of computational biology is to numerically describe data from a system, but also to find ways of fixing it and to understand a system’s objectives, algorithms, and mechanisms. Here we will argue that, regardless the objective, machine learning should be a central contribution to progress in every flavor of biomedical science. Machine learning can typically better describe the data. In doing so it can also provide a benchmark for any other way of describing the data. Using examples from neuroscience we discuss how better performance matters for decoding models and how having a benchmark affects encoding models. Similar issues matter in medicine. As biomedical science evolves, machine learning is morphing into a critical tool across the full spectrum of scientific questions.

Bio
Konrad Kording is a Professor at the University of Pennsylvania in the Departments of Bioengineering and Neuroscience. He received his PhD in Physics from ETH Zurich and did postdoctoral training at ETH Zurich, UCL London, and MIT focusing on statistical approaches to the brain and cognition. He is using data science towards understanding the brain, medicine, and scientists themselves.

November 16th

Abstract
Social networks, search engines, mobile apps, IoT vendors, online entertainment, and e-commerce sites have lead the way in using an individual’s digital traces to tailor service offerings, improve system performance, and target advertisements. A growing community of researchers are looking to these same data sources to create digital biomarkers for use in personalized health and wellness applications. This talk will discuss motivation and progress.

Bio
Deborah Estrin (PhD, MIT; BS, UCB) is a Professor of Computer Science at Cornell Tech in New York City where she founded the Jacobs Institute’s Health Tech Hub. She is also a co-founder of the non-profit startup, Open mHealth. Her current focus is on mobile health and small data, leveraging the pervasiveness of mobile devices and digital interactions for health and life management (TEDMED http://smalldata.io). Previously, Estrin was the founding director of the NSF-funded Science and Technology Center for Embedded Networked Sensing (CENS) at UCLA (2002-12). Estrin is an elected member of the American Academy of Arts and Sciences (2007) and National Academy of Engineering (2009). She was awarded honorary doctorates from EPFL and Uppsala.